Open-source alternatives to From Python To Numpy

Useful extensions to the standard Python datetime features

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This project is a pandas data analysis cookbook and Python data science guide. It provides a collection of programmatic recipes and examples for cleaning, manipulating, and analyzing structured data. The project focuses on providing a containerized analysis environment to ensure a consistent workspace and reproducible dependencies when executing data processing scripts. It covers a broad range of data science capabilities, including data ingestion from external sources, raw data cleaning, and exploratory data analysis. These recipes demonstrate how to perform structured data analysis through

Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h

Pandarallel is a Python library designed to accelerate data manipulation workflows by distributing computationally intensive tasks across all available CPU cores. It functions as a parallel processing utility that integrates directly into standard data analysis pipelines to reduce the execution time of large-scale operations. The library employs a wrapper pattern that intercepts standard data processing calls, redirecting them from sequential execution to a parallelized architecture. It manages this distribution through a multiprocessing-based task model, which utilizes shared-memory serializ

Numba is a just-in-time compiler that translates high-level Python functions into optimized machine code at runtime. By leveraging the LLVM compiler infrastructure, it provides a framework for accelerating numerical data processing and mathematical computations, enabling performance levels comparable to statically compiled languages. The project distinguishes itself through its ability to perform type-inference-based specialization, which generates machine instructions tailored to the specific data types used during execution. It employs a lazy compilation pipeline that defers translation unt

NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing and manipulating large-scale numerical information. It centers on high-performance multidimensional array objects that serve as the primary data structure for complex mathematical operations and data analysis workflows. The library distinguishes itself through specialized mechanisms for handling multidimensional data, including advanced indexing, slicing, and broadcasting techniques that allow for efficient operations across arrays of varying shapes. It utilizes strided metadat

Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning, and transforming structured datasets. It centers on labeled one-dimensional and two-dimensional data structures, allowing users to construct, filter, and reshape tabular information while performing complex arithmetic and logical operations. The library distinguishes itself through a sophisticated indexing engine that enables automatic data alignment during calculations and relational merges. By utilizing a block-based memory layout, it optimizes cache locality for vectorized

These are public type stubs for pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of pandas. In general, these stubs are narrower than what is possibly allowed by pandas, but follow a convention of…

petl - Extract, Transform and Load

Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e

Extract data from a wide range of Internet sources into a pandas DataFrame.

A Python tool that automatically cleans data sets and readies them for analysis.

This project is a curated collection of programming exercises designed to build proficiency in numerical computing and data manipulation. It provides a structured learning path for mastering multidimensional array operations, vectorized arithmetic, and statistical analysis. The repository focuses on developing practical expertise in array-based workflows, emphasizing techniques such as memory management, efficient data processing, and the replacement of explicit loops with vectorized operations. Users engage with hands-on challenges that cover the full lifecycle of numerical data, from initia

Pandas integration with sklearn

Pendulum is a Python date and time library that provides a simplified interface for date arithmetic, ISO parsing, localization, and timezone management. It serves as a replacement for the standard library datetime module, extending existing classes to maintain compatibility while offering a more intuitive management interface. The library distinguishes itself through a natural language date formatter that translates time differences into localized strings and a dedicated ISO 8601 date parser. It also includes tools for time-dependent logic testing, allowing the system clock to be mocked or fi

Fuzzy String Matching in Python

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

A collection of notebooks behind my series on writing idiomatic pandas.

Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin